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The objective of this paper is to present how to design a system that can accommodate additional functionality with either no changes to the design or adding architectural modules without changing the implementation of the legacy functionality. This objective is very relevant to industrial domains where an architecture is designed before the full range of functionalities to support is known. We focus on an important aspect of the design of automotive systems: the scheduling problem for hard real time distributed embedded systems. Two metrics are used to capture the design goals. The metrics are optimized subject to a set of constraints within a mathematical programming framework. The cost of modifying a legacy system is characterized at an electrical control unit (ECU) component level. Results obtained in automotive applications show that the optimization framework is effective in reducing development and re-verification efforts after incremental design changes.